It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of up- or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework...

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to...

This paper presents an empirical analysis of the relationship between fiscal illusion and the shadow economy for 104 countries over the period 1989-2009. We argue that both unobservable phenomena are closely linked to each other, as the creation of a fiscal illusion may be helpful if governments...

Persistent variations in the log price-to-dividend ratio (PtDR) have triggered a lively discussion in the literature. This paper proposes a present value model incorporating this persistence through a time-varying steady state of the PtDR. Log-likelihood statistics confirm that the time-varying...

A central question for monetary policy is how asset prices respond to a monetary policy shock. We provide evidence on this issue by augmenting a monetary SVAR for US data with an asset price index, using set-identifying structural restrictions. The impulse responses show a positive asset price...

We assess the contribution of macroeconomic uncertainty -- approximated by the dispersion of the real GDP survey forecasts -- to the ex post and ex ante prediction of stock price bubbles. For a panel of six OECD economies covering 24 years, two alternative binary chronologies of bubble periods...

Apart from a priori assumptions on instantaneous or long run effects of structural shocks, sign restrictions have become a prominent means for structural vector autoregressive (SVAR) analysis. Moreover, second order heterogeneity of systems of times series can be fruitfully exploited for...

Multivariate Volatility Models belong to the class of nonlinear models for financial data. Here we want to focus on multivariate GARCH models. These models assume that the variance of the innovation distribution follows a time dependent process conditional on information which is generated by...